1. Field of the Invention
The present invention relates to a handwriting recognition device; and more particularly, to a handwritten numeral recognition device using fuzzy logic and cellular neural network.
2. Description of Related Art
The influx of personal digital assistants (PDA) and touch-screen electrical appliances has created a demand for a small, instantly-recognizable, and robust handwritten character translator (HCT). Handwriting recognition devices are devices that attempt to identify handwritten images based upon an existing character in order to output recognized images. One inherent difficulty of applications related to handwritten character recognition is that handwritten characters are variable on an individual basis. Moreover characters are usually separated into alphabets, numerals, and symbols despite the difference characters of the language itself. Therefore it is advantageous to separate the handwriting recognition into individual systems for increasing its speed, robustness, and efficiency.
In order to accommodate handwritten recognition devices to an individual's handwriting style, typical handwritten recognition devices will firstly operate in software using a trained character, whereby a user will place the device in a training mode and enter in the user's numeral on a regular basis via a graphic user interface, such as a digital writing tablet. The numerals input by the user are then used as a template for use in comparing other numerals that are handwritten by the user via the graphical user interface. In such a case, however, the operation of the existing handwritten character recognition devices is based on the assumption that an individual's writing style is relatively uniform. Thus, if a user's handwriting varies due to fatigue or stress, the handwritten characters may be unrecognizable. Thus, to ensure recognition, a user may often be required to write slowly and carefully. In addition not only do individuals have different handwriting styles, but an individual's own handwriting may change over time and moreover the characters may be smaller or larger in different instances. Devices attempting to perform handwritten character translation may make incorrect decisions due to an inability to accommodate a variety of handwritten character shapes and sizes. In addition, these devices would be relatively slow due to the software implementation. Finally, the existing handwritten character recognition devices suffer the disadvantage of being user-specific, and would not work for another user because of the individual variations in handwriting.
Because of the fuzzy nature of human handwriting, it makes sense to adapt “fuzzy logic” into the handwriting recognition device. The “fuzzy logic” control theory is well known in the art of data processing. Rather than evaluating the two values “TRUE” and “FALSE” as in digital logic, fuzzy terms admit to degrees of membership in multiple sets so that fuzzy rules may have a continuous, rather than stepwise, range of truth of possibility. Therefore non-identical handwritten numeral from same or different users can be approximated using fuzzy logic for fast and robust handwriting recognition. The conventional method of fuzzy recognition relies upon the maximum membership principle:μΛ1 (μ0)=max{μΛ1(μ0), μΛ2(μ0), Λ, μΛN(μ0 )}  (1)therefore μ0⊂A1, where A1,A2,Λ,AN are N standard characters μ0 is the object to be recognized and N is the total number of classes. This conventional method is a single factor equation and can only recognize the handwritten character as a whole and find the closest standard character for to-be-recognized character. Therefore the conventional method is very restricted in terms of speed, robustness, and accuracy. To enhance system performance, it is necessary to find two or more of the closest standard characters for the to-be-recognized character and the standard characters.
Generally, analog fuzzy logic is constructed by multi-value logic circuit units, which may be of a voltage type or a current type. For conventional voltage type circuits, operational amplifiers are required for summation or subtraction operations to voltages, which makes the circuit complicated. On the contrary, the current type circuit is capable of proceeding summation and subtraction operations to currents and thus simplifies the circuit and is used in present invention. In addition, the operating speed of a current type circuit is generally higher than that of the voltage type circuit because the gain bandwidth of the operational amplifier restricts the operating speed of the voltage type circuit. Moreover, in a voltage type fuzzy logic circuit, switch capacitors are usually required, which increases the size of a chip for the circuit because a large chip area is required to fabricate a capacitor. The use of switch capacitors also increases the complexity of manufacturing a chip for the circuit as two polysilicon layers are required for fabricating a switch capacitor. The fabrication of a current switch for the current type fuzzy logic can be done by standard digital CMOS technology and thus reduce the complexity of manufacturing a chip for the circuit. Accordingly, the present invention provides a switch current type fuzzy processor for high-speed character recognition.
Furthermore, a handwritten character is mostly unique even written by the same user because it can vary in size, magnitude, curvature, stroke, and etc . . . Therefore a fuzzy logic handwritten character translator itself is not sufficiently fast and robust in recognizing the entire character. As a result, it is a necessary to include a feature extractor that capable of high-speed parallel signal processing. Cellular neural network (CNN) is the best choice because it allows real-time signal processing found within a digital domain and local interconnection features for VLSI implementation. A feature extractor uses a 24×24 pixels CNN to perform extraction of the handwritten numeral image. The extracted image is then decoded into various feature groups consisting of different feature, a feature is the primitive of a character such as a dot, line, or curve.